 prototype model
 usage of prototype model in problem solving
 numerical/combinatorial optimisation
 problem structure
 optimization types
 problem types (decision, optimisation and combined problem)
 traditional combinatorial problems
 SAT problem
 TSP problem
 GC problem
 searching for solutions
 search as a solving paradigm
 perturbative and constructive search
 systematic and local search
 combination of search types
 search type selection
 systematic search
 DPLL  basic characteristics
 DPLL structure
 DPLL pruning (unit propagation, pure literal)
 DPLL  selection heuristics
 DPLL  conflict analysis (clause learning, backtracking)
 local search
 characteristics of local search
 neighbourhood (role, definition) and neighbourhood graph
 basic components of local search (init, step, term)
 structure of local search (decision and optimisation problem)
 search trajectory
 iterative improvement
 informed vs. uninformed search strategy
 evaluation function
 iterative improvement (basic components)
 structure of iterative improvement
 local optimum curse
 escape from local optima
 random restarts (characteristics, structure)
 larger neighbourhood (pruning, pivot rules)
 neighbourhood switching
 VND (variable neighbourhood descent) structure
 step beyond neighbourhood (step construction in LK)
 VDS (variable depth search) structure
 acceptance of inferior candidates
 RII (randomised iterative improvement) characteristics
 RII structure
 PII (probabilistic iterative improvement) characteristics
 PII structure
 tabu search (short term memory design)
 enhancements of tabu search (acceptance criterion, long term memory)
 TS (tabu search) structure
 dynamic changes of evaluation function
 DLS (dynamic local search) structure
 hybrid search algorithms
 hybrid strategies
 ILS (iterated local search) characteristics and structure
 GRASP (greedy randomised adaptive search procedure) characteristics (greedy vs random principle)
 GRASP structure
 GRASP  constructive phase (greedyrandom, randomgreedy, random+greedy)
 pathrelinking (principle, usage in GRASP)
 AICS (adaptive iterated construction search) characteristics and structure
 biologically inspired algorithms  ant system
 real ant characteristics, shortest path search
 AS (ant system) as a search algorithm
 AS  artificial ant world
 AS  selection of move direction
 AS  pheromone maintenance
 AS structure
 AS for MAXSAT (world topology and dynamics)
 biologically inspired algorithms  bacterial foraging
 movement vs food search (general animal, bacteria)
 movement of E.coli (basic movement, external influence, social influence)
 mapping between E.coli movement and BFO (bacterial foraging optimisation) algorithm
 BFO  step function
 BFO structure
 BFO  reproduction and dispersion
 BFO  social behaviour
 biologically inspired algorithms  bee colonies
 model of bee foraging, waggle dance
 mapping between bee colony and ABC (artificial bee colony) algorithm
 ABC  simplified model of bee colony
 ABC  principles
 ABC structure
 ABC  source exploitation
 ABC  as a search algorithm
biologically inspired algorithms  evolutionary algorithms
natural selection and its model
genetic transfer and its model
mapping between evolution process and EA (evolutionary algorithm)
EA structure
 probability foundations
 probability distributions (joint, marginal, conditional)
 variable (in)dependence
 joint distribution factoring
 estimation of distribution algoritms
 characteristics of EDA (population sample vs model)
 EDA structure
 probabilistic models
 UMDA (univariate marginal distribution algorithm) characteristics
 UMDA structure
 MIMIC (mutual information maximizing input clustering) characteristics
 MIMIC structure
 BMDA (bivariate marginal distribution algorithm) characteristics
 BMDA structure
 computational complexity
 theory of computational complexity
 complexity of algorithms and problems
 P and NP complexity classes
 NPhard and NPcomplete complexity classes
 complexity of combinatorial problems
 solving NP problems
 stochastic versus deterministic search
 associated approximation problem
 approximation complexity classes (APX, PTAS, FPTAS)
randomised algorithms, MAXSAT randomised algorithm
 selected algorithms
 constructive heuristics for TSP
 GSAT family (GSAT, GWSAT, HSAT, HWSAT, ...)
 GC heuristics (sequential, parallel)
